Public access from Dec 1, 2020
Consortium of Swiss Academic Libraries

DIFFERENCING TRANSFORMATIONS AND INFERENCE IN PREDICTIVE REGRESSION MODELS

Camponovo, Lorenzo

In: Econometric Theory, 2015, vol. 31, no. 6, p. 1331-1358

Université de Fribourg

A wild bootstrap algorithm for propensity score matching estimators

Huber, Martin ; Camponovo, Lorenzo ; Bodory, Hugo ; Lechner, Michael

(Working Papers SES ; 470)

We introduce a wild bootstrap algorithm for the approximation of the sampling distribution of pair or one-to-many propensity score matching estimators. Unlike the conventional iid bootstrap, the proposed wild bootstrap approach does not construct bootstrap samples by randomly resampling from the observations with uniform weights. Instead, it fixes the covariates and constructs the bootstrap...

Université de Fribourg

The finite sample performance of inference methods for propensity score matching and weighting estimators

Bodory, Hugo ; Huber, Martin ; Camponovo, Lorenzo ; Lechner, Michael

(Working Papers SES ; 466)

This paper investigates the finite sample properties of a range of inference methods for propensity score-based matching and weighting estimators frequently applied to evaluate the average treatment effect on the treated. We analyse both asymptotic approximations and bootstrap methods for computing variances and confidence intervals in our simulation design, which is based on large scale labor...

Università della Svizzera italiana

Robust subsampling

Camponovo, Lorenzo ; Scaillet, Olivier ; Trojani, Fabio

In: Journal of econometrics, 2012, vol. 167, no. 1, p. 197-210

We characterize the robustness of subsampling procedures by deriving a formula for the breakdown point of subsampling quantiles. This breakdown point can be very low for moderate subsampling block sizes, which implies the fragility of subsampling procedures, even when they are applied to robust statistics. This instability arises also for data driven block size selection procedures minimizing ...

Università della Svizzera italiana

Robust resampling methods and stock returns predictability

Camponovo, Lorenzo ; Trojani, Fabio (Dir.)

Thèse de doctorat : Università della Svizzera italiana, 2009 ; 2009ECO004.

The thesis consists of three chapters. In the first chapter we characterize the robustness of subsampling procedures by deriving a general formula for the breakdown point of subsampling quantiles. This breakdown point can be very low for moderate subsampling block sizes, which implies the fragility of subsampling procedures, even if they are applied to robust statistics. This instability...